arXiv:2505.21723v3 Announce Type: replace-cross
Abstract: In the era of AI, neural networks have become increasingly popular for modeling, inference, and prediction, largely due to their potential for universal approximation. With the proliferation of…
arXiv:2604.02738v1 Announce Type: cross
Abstract: This paper focuses on the state estimation problem in distributed sensor networks, where intermittent packet dropouts, corrupted observations, and unknown noise covariances coexist. To tackle this chal…
arXiv:2604.02969v1 Announce Type: cross
Abstract: The natural gradient method is widely used in statistical optimization, but its standard formulation assumes a Euclidean parameter space. This paper proposes an inversion-free stochastic natural gradie…
arXiv:2510.02513v2 Announce Type: replace-cross
Abstract: Adaptive randomized pivoting (ARP) is a recently proposed and highly effective algorithm for column subset selection. This paper reinterprets the ARP algorithm by drawing connections to the vol…
arXiv:2506.06845v2 Announce Type: replace-cross
Abstract: Linear discriminant analysis (LDA) is a fundamental classification and dimension reduction method that achieves Bayes optimality under Gaussian mixture, but often struggles in high-dimensional …
arXiv:2405.15132v5 Announce Type: replace
Abstract: The Intrinsic Dimension (ID) is a key concept in unsupervised learning and feature selection, as it is a lower bound to the number of variables which are necessary to describe a system. However, in a…
arXiv:2508.12627v2 Announce Type: replace
Abstract: Higher-order $U$-statistics abound in fields such as statistics, machine learning, and computer science, but are known to be highly time-consuming to compute in practice. Despite their widespread app…
arXiv:2603.26820v1 Announce Type: cross
Abstract: Digital twins for radiation-based imaging and therapy are most useful when they assimilate patient data, quantify predictive uncertainty, and support clinically constrained decisions. This paper presen…
arXiv:2603.27415v1 Announce Type: new
Abstract: Classical optimization algorithms–hill climbing, simulated annealing, population-based methods–generate candidate solutions via random perturbations. We replace the random proposal generator with an LL…
arXiv:2603.28423v1 Announce Type: cross
Abstract: We introduce a novel class of graphical models, termed profile graphical models, that represent, within a single graph, how an external factor influences the dependence structure of a multivariate set …